Gut microbial community in proboscis monkeys (Nasalis larvatus): implications for effects of geographical and social factors

Recent technological advances have enabled comprehensive analyses of the previously uncharacterized microbial community in the gastrointestinal tracts of numerous animal species; however, the gut microbiota of several species, such as the endangered proboscis monkey (Nasalis larvatus) examined in this study, remains poorly understood. Our study sought to establish the first comprehensive data on the gut microbiota of free-ranging foregut-fermenting proboscis monkeys and to determine how their microbiota are affected locally by environmental factors, i.e. geographical distance, and social factors, i.e. the number of adult females within harem groups and the number of adults and subadults within non-harem groups, in a riverine forest in Sabah, Malaysian Borneo. Using 16S rRNA gene sequencing of 264 faecal samples collected from free-ranging proboscis monkeys, we demonstrated the trend that their microbial community composition is not particularly distinctive compared with other foregut- and hindgut-fermenting primates. The microbial alpha diversity was higher in larger groups and individuals inhabiting diverse vegetation (i.e. presumed to have a diverse diet). For microbial beta diversity, some measures were significant, showing higher values with larger geographical distances between samples. These results suggest that social factors such as increased inter-individual interactions, which can occur with larger groups, as well as physical distances between individuals or differences in dietary patterns, may affect the gut microbial communities.


Introduction
The gastrointestinal tracts of animals are colonized by many microorganisms, forming complex microbial ecosystems [1][2][3].In general, animal-associated microbial ecosystems have been reported to have a direct effect on host health, contributing not only to daily energy acquisition through the production of vitamins and short-chain fatty acids but also to the host's immune system and resistance to pathogens [4][5][6].Understanding how the microbial community, which serves important functions in animals, is formed in the gastrointestinal tracts would shed light on the survival strategies of diverse animal species.Historically, the study of the human gastrointestinal microbial community has been extensive [7,8].However, with recent advancements in sequencing technology, the ability to analyse gastrointestinal microbial diversity and community structure based on large amplicon libraries of 16S ribosomal RNA (rRNA) genes, primarily using faecal DNA, has prompted additional research in a variety of non-human primates [9][10][11][12].With the recent accumulation of such research findings in non-human primates, it is becoming increasingly evident that their living environment influences and shapes the gastrointestinal microbial community [13].For example, within the same primate species, a correlation has been observed between the diversity of dietary items and the microbial community [14][15][16][17].Alternately, as in primate species that live in groups, it has been hypothesized that social factors may influence the establishment of the microbial community, as the horizontal transmission of the microbiota may occur via direct social interactions between individuals within the group or even indirect interaction via shared environments [18][19][20][21].
Although the gastrointestinal microbial community, particularly the gut microbiota from faecal samples, has been progressively studied in various primate species over the last decade [10,[22][23][24][25], there are still numerous species in the wild for which even the most fundamental microbial community has not been studied.The foregut-fermenting proboscis monkey (Nasalis larvatus), a large, sexually dimorphic, arboreal primate [26], is one of these species for which the hindgut microbiota has not been studied in the wild (but see in captivity [27]), though comprehensive analyses of their foregut microbiota have been reported in the wild [15].As both the foregut and hindgut have been reported to facilitate the digestive fermentation of dietary fibre in the closely related foregut-fermenting primate species, i.e.Rhinopithecus roxellana [23], a comprehensive analysis of hindgut bacteria based on faecal samples in proboscis monkeys is essential for fully understanding their digestive physiology.
Proboscis monkeys inhabit various riparian and coastal forest environments, including riverine, mangrove and peat swamp forests [28], and their dietary patterns have been reported to be flexibly adapted to these environments; diverse diets in riverine forests with higher plant diversity, and low dietary diversity in mangrove forests where plant diversity is extremely low [29][30][31].Owing to its adaptable feeding habits, the proboscis monkey is an ideal study species for determining how habitat-specific dietary differences influence gut microbiota.The basic component of social structure in proboscis monkeys is as polygynous single male societies (harem group) that assemble with each other in the trees along rivers [32][33][34][35], though all-male groups and, occasionally, groups containing more than one male with multiple females are also found [36].Consequently, it is a fascinating species with which to investigate the relationship between social factors such as different group types with different sexual compositions of members and the intestinal microbial community.
We sought to establish the first comprehensive data on the gut microbiota of free-ranging proboscis monkeys and to determine how these gut microbiota were affected at the local scale by environmental factors (e.g.geographical location) and social factors (e.g.sexes with different life histories and group size).In particular, we hypothesize that the observed richness and diversity of gut microbial communities would vary based on the distance of their living areas from the river mouth in the study site and the number of adult females in harem groups.In addition, we propose that beta diversity measures of gut microbiota would correlate with geographical distance between individuals.Previous research has demonstrated that the gut microbiota is frequently influenced by dietary habits.Therefore, we anticipated that there would be differences between the gut microbiota of proboscis monkeys living near the river mouth, where deforestation by oil palm plantations is more pronounced, and those living in the upper river areas (see electronic supplementary material, S1), where relatively large areas of forest remain, at our study site [29].Indeed, since increased dietary diversity has been reported to increase the diversity of the gut microbial community (e.g.[15,37,38]), it would be predicted that disturbed habitats would generally experience a reduction in plant diversity [39] and that the monkeys inhabiting these habitats would experience a reduction in dietary diversity.Furthermore, sex differences in social behaviour have been associated with sexual biases in the gut microbiota in primates (e.g.[19,40,41]), and female-to-female grooming is the predominant form of grooming in proboscis monkeys, but rarely between males and females [42,43].Thus, female proboscis monkeys are expected to have more contact with more individuals in the group and, accordingly, alpha diversity and composition of their gut microbiota may be expected to be more diverse and/or of higher similarity than males.Finally, given that several reports in primates have stated that direct inter-individual contact is associated with the transmission of gut microbiota [18,21], it can be predicted that individuals with closer inter-individual distances would have more similar gut microbiota.Additionally, it is likely that individuals in larger groups would have more opportunities for social interaction with more individuals and, as a result, they may possess a more diverse gut microbiota.

Study site and subjects
The study was carried out in a riverine forest along the Menanggul River, a tributary of the Kinabatangan River, Sabah, Malaysian Borneo (118°30′ E, 5°30′ N), inhabited by eight species of diurnal primates, including our study species, the proboscis monkey.For more than a decade, this area has been a popular tourist destination that attracts boat tours; as a result, the proboscis monkeys were well habituated to human observers.The study site, in a 4 km stretch from the mouth of the Menanggul River upstream was home to at least 200 proboscis monkeys, organized into 8−10 harem groups with one adult male, multiple adult females and immatures [35,44], and various non-harem groups, including all-male groups (including solitary males), as well as other groups with multiple males and multiple females [36,45].The southern portion of the Menanggul River is dominated by secondary forests, while the northern part has been cleared for oil palm plantations, excluding a protected zone along the river (electronic supplementary material, S1).Daily temperatures in the area were recorded at approximately 24°C (minimum) and 30°C (maximum), with an average annual rainfall of 2474 mm [29].The river levels fluctuate by approximately 1 m daily, with seasonal floods causing an average increase of more than 3 m [46].

Faecal sampling
Proboscis monkeys in the lower Kinabatangan floodplain typically prefer to sleep along the river [47].Therefore, we conducted a boat survey in the late afternoon to detect proboscis monkeys and record their group composition with GPS coordinates of their sleeping trees.In the early hours of the following morning, while the monkeys were still sleeping, we revisited their sleeping trees.As proboscis monkeys typically defecate shortly before moving into the forest, we carefully searched the ground near their sleeping trees to collect fresh faeces after they had left the sleeping trees [35].Several harem groups often stayed in close proximity to the trees along the river, and it was sometimes difficult to determine which group the faeces on the forest floor belonged to, but based on the location of the faeces and the location of the group identified during the boat survey on the previous day, we inferred the group to which the individual that had defecated belonged.We only focused on collecting faecal samples presumed to be from adult individuals [48], and between June 2015 and April 2016, a total of 307 samples were opportunistically collected.However, owing to the nature of the faeces collection procedure, it was unclear how many groups in the study area the faeces were collected from.The collection was always carried out immediately after defecation, using a sterilized plastic spoon royalsocietypublishing.org/journal/rsos R. Soc.Open Sci.11: 231756 attached to the sampling tube.The spoons were inserted into fresh faeces, and only a small amount of the interior was removed and stored in 5 ml lysis buffer (0.5% sodium dodecyl sulfate, 100 mM ethylenediaminetetraacetic acid (pH 8.0), 100 mM Tris-HCl (pH 8.0) and 10 mM NaCl [16] at room temperature.To prevent duplicate analyses after genetic profiling, faeces from the same individual and those samples of unknown sex were excluded, and thus, the analysis of gut microbiota was performed with 264 faecal samples, i.e. 187 females and 77 males [35].

DNA purification, 16S ribosomal RNA amplification and sequencing
After bead-beating using the bead crusher (TAITEC, µT-01, Japan) and centrifuged at 4200 r.p.m. for 5 min, 200 µl of lysis buffer-faecal sample mixture was added with 800 µl of InhibitEX buffer of the QIAamp DNA Stool Mini Kit (Qiagen GmbH, Hilden, Germany).Next, the mixture was centrifuged at room temperature for 1 min at 13 000 r.p.m.Then, the lysate was transferred to a new 1.5 ml microcentrifuge tube with 25 µl of proteinase K.This was followed by adding 500 µl of Buffer AL and the manufacturer's protocols to purify the faecal DNA.Next, the DNA concentration was estimated with a Qubit dsDNA HS Assay Kit and a Qubit fluorometer (Thermo Fisher Scientific).We amplified the V3-V4 region of the 16S ribosomal RNA (rRNA) gene using the primer utilized in [49] with slight modification as follows: S-D-Bact-0341-b-S-17, 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-Ns-CCTACGGGNGGCW G-3′, and S-D-Bact-0785-a-A-21, 5′-GTCTCGTGGGCTCGGAGATGTGTA-TAAGAGACAG-Ns-GACTACHVG GG -3′.Subsequently, 3Ns, 4Ns, 5Ns and 6Ns were inserted in each primer between the specific primer and the adapter to cause an artificial frameshift and improve the sequencing quality [48].The mixture of these primers was used as forward and reverse primers at a concentration of 1 µM.KAPA Pure Beads (KAPA Biosystems, Wilmington, MA) were used to purify the polymerase chain reaction amplicons.The Illumina Nextera XT Index Kit (Illumina, Inc., San Diego, CA) was then used to attach specific dual indices and sequencing adapters to the amplicons for each sample.The resulting products were then combined in equal DNA concentrations to form a pooled sequencing library.Subsequently, the size distribution of the library was then estimated using an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., La Jolla, CA).The library was then diluted to a concentration of 15 pM and sequenced with a 15% PhiX spike-in on an Illumina MiSeq sequencing platform using the MiSeq Reagent Kit v. 3 (600 cycles) (Illumina, Inc., San Diego, CA).The resulting read lengths were 301 bp (forward sequences), 8 bp (forward indices), 8 bp (reverse indices) and 301 bp (reverse sequences).

Data analysis 2.4.1. Amplicon sequence variants picking and taxonomic identification
The demultiplexed sequences were processed using QIIME2 software [50].Amplicon sequence variants (ASVs) were generated using the DADA2 pipeline in this software through the dada2 plugin.In this step, the forward and reverse reads were merged.The unmerged reads were also discarded and the chimera was removed.The parameters used were to exclude demultiplexed sequences with a quality score below 30 from the downstream analysis to ensure high-quality data.The spurious ASVs were also removed by the QIIME2 dada2 plugin.The ASVs were assigned through the ribosomal database project classifier with GreenGenes v. 13_8 as the reference database for taxonomic identification.Additionally, we used the built-in function align-to-tree-might-fast tree of QIIME2 to construct a phylogenetic tree of the ASVs.

Statistical analysis
Statistical analyses were performed in R v. 4.1.1[51], with the significance level set at 0.05 using the unrarefied dataset with the singletons being removed.Results were reported as means with standard deviation.Alpha and beta diversity were calculated using the R package phyloseq [52].The Kruskal-Wallis test from the R package dunn.testwas used to analyse the differences in alpha diversity between sexes and group types.Subsequently, we investigated the effects of social and geographical factors on the microbial alpha diversity of individual samples using a linear model.The alpha diversity of each sample was treated as a normally distributed response variable, while the social factor, i.e. the number of adult females for harem groups and the number of adults and subadults for non-harem groups, and the geographical factor, i.e. the location of faecal samples collected along the river, which represented as the distance from the river mouth, were treated as explanatory variables.For all models, we verified that the variance inflation factors were smaller than the cut-off value, i.e. less than 10 [53].Therefore, the collinearity between independent factors (explanatory variables) did not affect the results.For the model selection, the possible combinations of the explanatory variables were examined and ranked using the Akaike information criterion (AIC) from the MuMIn package [54].Following the published guidelines for wildlife research, we generally selected the best-supported models as those with a ΔAIC score of less than 2, where ΔAIC = AIC − minimum AIC within the candidate models [55], but as suggested by Burnham et al. [56] the model with ΔAIC of range from 2 to 7 should not be dismissed; we discussed models that were in those ranges as well.
In our multivariate analysis of microbiota composition, we calculated Bray-Curtis dissimilarity, along with weighted and unweighted UniFrac indices with the R package vegan.We conducted the permutational multivariate analysis of variance (PERMANOVA, adonis2 function in the vegan package, R software, v. 4.1.1)tests to estimate the differences in terms of beta diversity.We constructed non-metric multi-dimensional scaling (NMDS) for visualization using Bray-Curtis dissimilarity and principal coordinate analysis (PCoA) plots by weighted and unweighted UniFrac indices with the plot ordination function from package ggplot2.In order to further assess the effect of social and geographical factors and to test their correlation with microbial beta diversity, Mantel tests were conducted by using the Mantel function.This function takes three parameters, where the first one is the first distance matrix, followed by the second distance matrix, and the last one is 'spearman' which indicates the method that we used to compute the correlation between these two distance matrices.The social factor, known as 'demographic distance', was calculated based on the composition of the differences in the group and the number of individuals within the groups, while the geographical factor, known as 'geographical distances', was calculated based on the distance between different faecal samples (or individuals).In other words, with respect to these two distances, values are higher with more differences in the pattern of group composition within a group (i.e.differences in the number of males and females) and with greater distances between different faecal samples (or individuals).

Phylogenetic profile of the faecal microbiota
In 264 faecal samples, we detected 16 530 ASV, classified into 29 phyla, 67 classes, 95 orders, 116 families, 134 genera and 50 species.The average number of sequences resulted per sample before filtering through DADA2 pipeline was 124 898, with a maximum of 287 114 and a minimum of 37 712.After filtering, the average was 100 900, with a maximum of 257 018 and a minimum of 10 234.
The best-fit model to explain the observed richness, as determined by AIC, included both the number of adult females in the harem groups and the location of the collected samples represented as the geographical distance from the mouth of the river (table 1 and figure 3a), although the ΔAIC value of the following model, which included only the number of adult females, was also less than 7.0.The observed richness increased as the number of adult females in the harem groups and the distance from the river mouth increased (upper river).The linear models for Shannon diversity index revealed a similar pattern to the observed richness in harem groups, with a positive effect on the number of adult females and geographical distance (table 1 and figure 3b).
Within the non-harem groups, however, the selection of the model for the investigation of whether the observed richness and Shannon diversity index were affected by social and geographical factors, i.e. the number of individuals, including adult males and females and subadult males, and the location of the collected samples, represented as the geographical distance from the river mouth, indicated that the null model was the best (table 2).The second-and third-best models, with observed richness and Shannon diversity index, incorporated social and geographical factors with ΔAIC values below the cut-off of 7.0.This result indicates that a similar pattern was generally observed in non-harem groups, albeit with weaker effects than in harem groups.
In order to examine the correlation between beta diversity measures and the two factors, i.e. geographical distances and social factors, the Mantel test was conducted separately on a dataset containing only samples from harem groups and one containing only samples from non-harem groups.Mantel tests performed with different beta diversity measures revealed significant positive correlations for the harem group between weighted UniFrac and geographical distances from different individuals (r = 0.09, p = 0.01) but not between unweighted UniFrac and geographical distances from different individuals (r = 0.02, p = 0.21).This indicated that individuals living further apart had a gut microbial community that was less similar, although the explanatory power of geographic distance was relatively weak.In contrast, Bray-Curtis dissimilarity measures of diversity, which do not account for the  1.
Table 1.Summary of model selection for harem groups using linear models.This method was used to investigate whether observed richness (A) and Shannon diversity index (B) were affected by social factors, i.e. the number of adult females within harem groups, and the geographical factor, i.e. the location of faecal samples collected along the river, which represented the distance from the river mouth.phylogeny of microbial composition, did not exhibit a significant correlation with geographic distance (r = 0.05, p = 0.09).In addition, there was no correlation between the different beta diversity measures and the distance of group composition between the various harem groups (weighted UniFrac: r = 0.10, p = 0.09; unweighted UniFrac: r = 0.02, p = 0.23; Bray-Curtis: r = 0.002, p = 0.48).
At the family level, the predominance of Ruminococcaceae or Lachnospiraceae is comparable to that of the foregut-fermenting R. brelichi and R. roxellanae.Macaca fuscata, a hindgut-fermenting primate, exhibited a similar trend [60], and Ruminococcaceae were frequently the most dominant in other hindgut-fermenting primates [12,61,62].Although the proportion taxonomically assigned at the genus level was limited, the top two genera, Oscillospira and Ruminococcus, are listed among the top five in P. nemaeus, a foregut-fermenter as well as in the proboscis monkey.Ruminococcus, in particular, is a common genus, as it frequently ranks first not only in foregut-fermenters but also in hindgut-fermenters [60,62].However, Blautia is not among the top genera in P. nemaeus, but it is among the top genera [60,62] in the hindgut fermenters.
In the light of these findings, the composition of the gut microbial community of the proboscis monkey is unlikely to differ significantly from that of other foregut-and hindgut-fermenting primates.However, when the microbiota is subdivided from the phylum level to the genus level, the degree of overlap obviously decreases, and species-specific microbial composition and bacterial species are found probably owing to more species-specific digestive physiology, dietary patterns related to living environment and/or, sociality of host animals, e.g.lactic acid bacteria, Lactobacillus nasalidis, specific only to the proboscis monkey [65,66].

Microbial patterns in relation to social factors
There were no differences in the alpha diversity index (number of ASV and Shannon diversity index) of the proboscis monkey gut microbiota between sexes or group types, indicating that alpha diversity is not affected by differences in social factors such as sex differences in life history and/or the social Table 2. Summary of model selection for non-harem groups using linear models.This method was used to investigate whether observed richness (A) and Shannon diversity index (B) were affected by social factors, i.e. the number of individuals, including adult males and females and subadult males within groups, and geographical factors, i.e. the location of faecal samples collected along the river, which represented the distance from the river mouth.Conversely, significant differences in beta diversity were observed between the sexes, suggesting that differences in life history and the frequency of social interactions may have influenced the composition of the gut microbiota.Significant differences in beta diversity between group types may also reflect differences of life history in the sexes; the basis for the differences in gut microbiota between group types with different male and female compositions within a group may be related to the differences in such life histories.At maturity, females transfer from their natal harem groups to other harem groups, whereas males disperse from their natal harem groups in the early stages to join all-male groups [32,42,67,68].Additionally, grooming within harem groups occurs primarily between females, with males rarely participating [43,69].Consequently, it is likely that these differences in the life histories of the sexes and in the frequency of social interactions between the sexes influence the composition of the gut microbiota in proboscis monkeys.Several other non-human primates have been reported to exhibit sexual biases in the gut microbiota, which could be attributed to differences in such interactions and life histories between sexes as a result of group living, e.g.
Callithrix jacchus [40], Rhinopithecus bieti [41] and Alouatta pigra [19], though such sexual differences are little in some species, e.g.Propithecus verreauxi [63].In contrast, it is difficult to determine its ecological significance without knowing how differences in microbiota composition affect factors such as food digestion and immunity.As expected, the positive correlation between the number of females in harem groups and the alpha diversity index suggests that increased individual interactions result in an increase in alpha diversity.This result is consistent with previous research indicating that direct physical contact between social partners is a major factor in the transmission of gut microbiota [19,21,40].

Microbial patterns in relation to geographical factors
In addition to the number of females within the harem groups, individuals who resided in areas further upstream of the river mouth at our study site had higher alpha diversity indices.This may be owing to differences in the food diversity consumed by proboscis monkeys in upstream and downstream regions.In general, individuals with a more varied diet have a greater variety of symbiotic bacteria in their gastrointestinal tracts [15,37,38].Indeed, given that the downstream area of the study site is more heavily affected by deforestation [29] and has often lower plant diversity with lower potential food sources for proboscis monkeys, especially on the north side of the river (electronic supplementary material, S9), individuals in the upstream area may have had access to a greater variety of food sources, resulting in a tendency for a higher alpha diversity index in their gut microbiota.Nevertheless, to strengthen this conclusion, differences in vegetation between upstream and downstream areas would require a more quantitative investigation and comparison with the degree of gut microbial diversity obtained in the present study.
In the non-harem groups, a similar relationship was observed between alpha diversity and the number of individuals in the group and the geographical conditions they inhabited, although this trend was weaker with insignificance.This difference may be attributable to the fact that non-harem groups, particularly all-male groups, exploit a broader range of riparian habitats than harem groups [35], which not only facilitate a more diverse diet but also direct/indirect interactions with conspecifics, coupled with increased such interactions with various organisms in the forest elsewhere, possibly leading to the horizontal transmission of the microbiota [70,71].However, not only the social relationships between individuals within non-harem groups but also those with various organisms remain unclear, making further discussion impossible at this time.The key to advancing this discussion in the future would be the collection of additional ecological and social observation data on non-harem groups.
There was a weaker but significant correlation between the physical distance between individuals and their similarity in the composition of their intestinal microbial communities, regardless of the group type.It should be noted, however, that the significance of the correlation varied slightly between the various group types based on the various indices, namely weighted UniFrac, unweighted UniFrac and Bray-Curtis.As previously mentioned, differences in vegetation between upstream and downstream areas of this study site may influence the alpha diversity of individual proboscis monkeys; it cannot be ruled out that the physical distance between individuals may lead to differences in diets consumed, which may, in turn, lead to differences in the composition of the intestinal microbial community between individuals.However, no correlation was found between similarities in group composition and those in gut microbial community composition.Compared with seasonal and clumped food sources, such as fruits and flowers, there was a higher ubiquitous and abundant 11 royalsocietypublishing.org/journal/rsos R. Soc.Open Sci.11: 231756 availability of leaves as the main food source for proboscis monkeys in this study site [31,72] and the lack of a clear hierarchy between individuals, which occurs particularly within harem groups [42].This may have contributed to the lower likelihood of dietary bias across group composition in proboscis monkeys, and hence, there might be no differences in the similarity of the composition of gut microbial community between individuals.Conversely, there have been no studies on the hierarchy among males in non-harem groups, particularly in all-male groups, and the finding that significant differences were observed in non-harem groups by all the beta dissimilarity indices may suggest that there may be a severe hierarchy among males, which resulted in differences in their dietary composition.To verify this, however, more comprehensive behavioural observations of all-male groups are required.

Outlook
We succeeded not only in determining the general trends in the intestinal microbiota of proboscis monkeys but also in determining how social and geographical factors affected this microbial community.In contrast, while we characterized such differences in the gut microbial community at the local level among individuals, we have not been able to elucidate how these differences act as an advantage to the survival of each individual.Future research must examine how differences in the gut microbial community between individuals influence their feeding strategy through more detailed functional analysis, such as by isolating and cultivating characteristic strains [66].Furthermore, considering that the gut microbiota also changes with diet in primates (e.g.[60,62,73,74]), there remains a need to compare the dietary patterns and gut microbiota of the individuals in a real-time manner, which we could not demonstrate in the current study, which was a limitation.When analysing gut microbiota from faeces, only a portion of the faeces is used for that analysis, and therefore many other portions are discarded without being used.However, with recent metabarcoding techniques [75][76][77], it may be possible to estimate dietary patterns from that remaining faecal sample.Hence, the analysis of inter-individual differences in the gut microbial community related to the different dietary patterns of each individual would be relatively feasible in the future study.Finally, the present study suggested that the degree of deforestation may influence the gut microbiota, although quantitative comparisons of these effects were not possible.While traditional vegetation surveys are often beneficial to understanding the relationship between environment and animal ecology [39,[78][79][80], advanced technologies such as the recently developing LiDAR technology [81] may allow more extensive and comprehensive habitat assessments which, when coupled with the analysis of gut microbial data of target animals as in the present study, may reveal more reliable interactions between their living environment and gut microbiota.In sum, with advancements in technology for the analysis of gut microbiota, it may be possible in the future to transition from non-invasively collected free-ranging primate faeces to studies that contribute to animal conservation, such as assessing the effects of forest disturbance.Multiple studies have demonstrated their potential use in animal conservation [15,17,37,82,83].
Ethics.This study was reviewed and approved by the Sabah Biodiversity Council (Access License Reference: JKM/ MBS.1000-2/2 JLD.5) and was conducted in compliance with animal care regulations applicable to Malaysian laws.This study is reported in accordance with ARRIVE guidelines (https://arriveguidelines.org).

Figure 1 .
Figure 1.The general pattern of the top five taxa.These were analysed at the phylum (a), family (b) and genus (c) levels.

Figure 2 .Figure 3 .
Figure 2. Comparison of the mean observed richness (a) and the Shannon diversity index (b) for different sexes and different group types.The central line of the box represents the median, and the lower and upper bounds of the box represent the first and third quartiles.